#find the nearest point from a given point to a large list of points

import numpy as np

def distance(pt_1, pt_2):
    pt_1 = np.array((pt_1[0], pt_1[1]))
    pt_2 = np.array((pt_2[0], pt_2[1]))
    return np.linalg.norm(pt_1-pt_2)

def closest_node(node, nodes):
    pt = []
    dist = 9999999
    for n in nodes:
        if distance(node, n) <= dist:
            dist = distance(node, n)
            pt = n
    return pt

a = []
for x in range(50000):
    a.append((np.random.randint(0,1000),np.random.randint(0,1000)))

some_pt = (1, 2)

closest_node(some_pt, a)

def closest_node(node, nodes):
    nodes = np.asarray(nodes)
    dist_2 = np.sum((nodes - node)**2, axis=1)
    return np.argmin(dist_2)

def closest_node(node, nodes):
    nodes = np.asarray(nodes)
    deltas = nodes - node
    dist_2 = np.einsum('ij,ij->i', deltas, deltas)
    return np.argmin(dist_2)

from numpy import random
from scipy.spatial import distance

a = random.randint(1000, size=(50000, 2))

some_pt = (1, 2)

a[distance.cdist([some_pt], a).argmin()]

a = []
for x in range(50000):
    a.append((np.random.randint(0,1000),np.random.randint(0,1000)))

a = np.random.randint(1000, size=(50000, 2))

from scipy.spatial.distance import cdist

def closest_node(node, nodes):
    return nodes[cdist([node], nodes).argmin()]

Original:
1 loop, best of 3: 1.01 sec per loop

Jaime v1:
100 loops, best of 3: 3.32 msec per loop

Jaime v2:
1000 loops, best of 3: 1.62 msec per loop

Mine:
100 loops, best of 3: 2.07 msec per loop